Such systems are substantially known per se and form the structure of the common-rail system of modern combustion engines. The pressure sensors arranged in the storage containers serve in particular for the failure analysis of such systems.
DE 199 46 506 C1 describes a method for detecting a malfunction in the pressure system of an engine wherein a pressure signal of a pressure sensor is registered in a temporally resolved manner so that periodic pressure variations caused by the operation of the injectors and the piston strokes of the piston pump can be monitored. The development of the pressure signal is measured and compared to a stored pattern so that a malfunction is deduced from a deviation of the pattern with respect to amplitude or periodicity. The difference between the largest and the smallest pressure measurement signal within a period is also determined. A malfunction in the system is again deduced should this difference differ from the stored pattern. The determination of a rate-of-discharge curve is not disclosed.
A method for monitoring the functionality of an injection system is described in DE 10 2005 004 423 B3 wherein a malfunction is deduced from a deviation from a set pressure curve using the measured pressure curve of a sensor arranged at the storage container. Both the time characteristic of the pressure and the absolute pressure are here considered. This method also does not allow the determination of an actual rate-of-discharge curve for individual valves.
A similar structure is described in DE 197 40 608 A1. In this case, however, a pressure curve in the storage container is detected with high resolution by the pressure sensor and a pattern is obtained from the pressure curve through which a fuel injection-related parameter, such as the injection volume of the injection time, is determined individually for each combustion chamber and each injection process. This is done via a neural network. However, this network first has to be trained at a test bench in order to obtain plausible results. The absolute values of the injection volumes are in particular impossible to determine without a previous learning process of the network. A separate learning process must thus be carried out for each engine so that an application in serial production is not feasible.